A novel underwater acoustic communication system based on chirp index modulation and convolutional neural network

Abstract In this paper, we present a novel underwater acoustic (UWA) communication system based on a multiple chirp rate shift keying with index modulation (MCrSK-IM) scheme and a convolutional neural network (CNN) based demodulator. The proposed system is designed to mitigate the problems of low data transmission rate and high bit error rate (BER) caused by severe multiple path propagation and Doppler effects in UWA channels. At the transmitter, the chirp-indexed modulation approach maps input bits to specific combinations of up and down chirp rates, improving spectral efficiency without increasing bandwidth. At the receiver, a trained CNN model performs adaptive feature extraction and accurate symbol classification, effectively replacing the traditional Fractional Fourier Transform, Matched Filter and Multilayer Perceptron detection scheme. Simulation results obtained from the static BELLHOP channel and the dynamic NOF1 channel from the Watermark UWA channel set demonstrate that the proposed MCrSK-IM-CNN system achieves reliable underwater data transmission at 2000 bit/s with a BER lower than 10 −3 under UWA channel conditions.